New deep AI‐based algorithm to assess quality of primary corneal endothelial cell cultures

Aims/Purpose: The development of cell therapies to treat corneal endothelial pathologies requires characterizing the cells during the cell culture process. The endothelial cell density (ECD) and morphometry are essential parameters for identifying cultures with sufficient phenotype and yield. However, they are difficult to measure on cells in culture using conventional image analysis tools. Aims: (1) To develop an AI‐based automatic cell segmentation method for reliable ECD and morphology measurement. (2) To select new morphometry parameters. (3) To classify endothelial cell cultures by quality. Methods: Experiments were conducted on primary cultures of human corneal endothelial cells (hCECs) after immunofluorescent labeling against NCAM revealing lateral membranes. Cultures from donors